Inertial Measurement Units (IMUs) for many applications require high performance gyroscopes and accelerometers to measure angular rate and linear acceleration with high precision and accuracy over a wide dynamic range. Traditionally this has been accomplished with exotic technologies such as mechanical or optical gyroscopes which result in large, power-hungry, and expensive IMUs.
In the past two decades there has been tremendous progress in the development of inertial sensors using micro-electromechanical systems (MEMS). MEMS inertial sensors can be used to produce IMUs with an enormous reduction in size, weight, power and cost. However, MEMS inertial sensors have not yet achieved sufficient performance to replace traditional IMUs in many guidance, navigation and control applications. So far, any individual MEMS inertial sensor has a shortfall in at least one aspect of performance, such as resolution, bias stability, dynamic range, temperature sensitivity, vibration sensitivity, or long-term bias or scale factor repeatability.
Redundant IMU (RIMU) configurations, utilizing more than three gyroscopes or accelerometers to determine angular rate or linear acceleration about three axes, have been used since the early days of inertial navigation to facilitate fault detection and isolation (FDI) in high-reliability aerospace applications. An explanation can be found in Cho and Park, “A calibration technique for a redundant IMU containing low-grade inertial sensors”, ETRI Journal, Vol 27, No 4, August 2005 and its references.
More recently, a few approaches have been proposed using arrays of identical MEMS gyroscopes, with a reduction of noise and/or drift errors achieved by averaging all their outputs together [e.g., Reynolds et al, U.S. Pat. No. 7,650,238, Bayard and Ploen, U.S. Pat. No. 6,882,964]. The average may be weighted according to the relative quality of the different gyros, but because it involves some form of averaging of all the gyros, it cannot overcome the weaknesses of each individual gyro as effectively as the method described below.
Lapinski [“A wearable wireless sensor system for sports medicine”, Michael Lapinski, MS. Thesis, MIT Media Lab, September 2008] built a system for monitoring baseball batters which includes both high-g accelerometers (necessary for the high dynamic range of sports motion) and low-g accelerometers which he added due to a concern about the limited resolution of the high-g accelerometers.